Several applications need detecting one or several predetermined tone(s) within received data. Such is the case when an alarm tone should be detected embedded within received data at a receiving station within a network like for instance in applications where a test procedure should be initiated subsequent to said tone detection. The test procedure may require starting with turning the whole network off, thus obviously any false tone detection should be avoided.
Another application relates to Multi-Frequency receivers (MFR) wherein signalling tones combining two single frequency waveforms should be detected in a Touch Tone telephone network.
The use of Touch Tone is becoming wide spread. These phones enable the users to send data (12 or 16 digits) over the telephone lines to a receiver which can then take an action according to the sequence of tones.
The store and forward system is a good example : Calls to a subscriber are stored on disks while he/she is not reachable. The subscriber can then later call the system and by sending digits identify him/herself, retrieve any stored messages or forward the messages to a third person.
In order to do so the system must be able to differentiate the digits. Furthermore, the electrical signals representing the digits should be well defined and eventually normalized, by the CCITT for instance.
An easy and inexpensive way to generate digits is to represent them by the sum of two sinusoidal signals at different frequencies. EQU x(t)=A1.multidot.sin(2.multidot..pi..multidot.f1.multidot.t+.phi.1)+A2.mult idot.sin(2.multidot..pi..multidot.f2.multidot.t+.phi.2)
The frequencies will then be detected by the receiver and by table look up or any other means the digit will be recognized.
Traditionally Multi-Frequency detection is handled in one of the following ways.
By using a specialized chip operating on the analog signal. But this requires a specialized circuit which increases the cost of the required equipment.
Another alternative requires sharp and accurate filters or banks of filters. These filters may be digitally implemented using a signal processor.
A third approach may be based on FAst Fourier Transforms (FFT) or DFT also implemented in a signal processor, over samples of the received signal to be processed.
For instance, let x(n), [n=0, . . . ,N-1] denote the input signal corresponding to a block of N samples.
The FFT is a fast evaluation of the DFT of the sequence x(n) defined by: ##EQU1## where .pi.=3.14 and
SUM standing for a summing or accumulating operation.
Thus, the filter or FFT based methods involve a high processing load, equivalent to several MIPS (say 2 to 4 MIPS) which may correspond to up to 40% of a signal processor capability.
Improved digital methods have already been proposed bringing the processing load to a little less than 10%.
The present invention further lowers the required processing load to about 1 to 5% of the considered signal processor once associated to a linear Prediction Coder.